library(haven)
library(tidyverse)
library(readxl)
library(tigris)
library(tidycensus)
library(sf)
library(leaflet)
library(gridExtra)
library(RColorBrewer)
library(kableExtra)
library(broom.mixed)
library(modelsummary)
options(tigris_use_cache = TRUE)
options(progress_enabled = FALSE)Another comparison of two area-level socioeconomic deprivation indices: SVI (Social Vulnerability Index) and ADI (Area Deprivation Index) and their associations with asthma and smoking outcomes from survey data
BMIN503/EPID600 Final Project
This report is still under construction, but ready for peer feedback.
# from https://rdrr.io/github/bradisbrad/olfatbones/src/R/get_tri.R
#' Triangularize a Correlation Matrix
#' @description Takes a correlation matrix and removes the redundant information
#' @param cormat Correlation Matrix
#' @param lower Lower or upper half (default = T) select F for upper half
#'
#' @return Matrix
#' @export get_tri
#'
#' @examples
#' cormat <- round(cor(mtcars), 2)
#' get_tri(cormat)
#'
get_tri <- function(cormat, lower = T){
if(lower){
cormat[upper.tri(cormat)] <- NA
} else {
cormat[lower.tri(cormat)] <- NA
}
cormat
}1 Overview
The goal of this project is to compare the Agency for Toxic Substances and Disease Registry Social Vulnerability Index (SVI) and the University of Wisconsin-developed Area Deprivation Index (ADI) in their association with asthma and smoking outcomes from the Behavioral Risk Factor Surveillance System (BRFSS) CDC dataset.
For SVI, the most recent available dataset is from 2020. For this project, national ranking at the county level will be used. The most recent ADI dataset is from 2021. National ranking for ADI is provided at the census block group level. For the asthma and smoking outcomes in the BRFSS CDC dataset, the 2021 Selected Metropolitan/Micropolitan Area Risk Trends (SMART) subset will used. The SMART BRFSS dataset provides the CBSA (Core-based statistical area), which “consist of the county or counties (or equivalent entities) associated with at least one core (urban area) of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties.” https://www.census.gov/programs-surveys/metro-micro/about/glossary.html
2 Introduction
As Rollings et al 2023 discuss, there exist several area-level socioeconomic deprivation indices without consensus within healthcare research and policy fields about which indices should be used for a variety of analyses. In comparing two frequently used indices, SVI and ADI, in their associations with SMART BRFSS dataset, this project has the potential to inform selection of the appropriate index for future work. This project is interdisciplinary in that it spans data science, social science, and biomedical informatics.
A challenge for this project, as with many projects which incorporate geospatial variables, is differing geographic units. For this project, SVI is at the county level, ADI is at the census block group level, and the BRFSS outcomes are at the CBSA level. Fortunately, all of these geographic units are census-defined and are supersets or subsets of each other. The current plan is to link these datasets at the county level. Based on guidance from Dr. Sherrie Xie, ADI will be aggregated to the county-level using a weighted average that incorporates census counts for census block groups. The BRFSS smoking and asthma outcomes will be aggregated at the CBSA level. CBSAs and CBSA level smoking and asthma outcomes will be mapped to counties. An alternative approach uses weighted averages to aggregate both SVI and ADI to the CBSA level, which can also be explored.
Once the linked dataset is prepared, regression models will be used to compare SVI and ADI as predictors of the aggregated smoking and asthma outcomes. Spatial autoregressive regression, as used by Tipirneni et al 2022 will also be considered. For smoking, the smoking status variable _SMOKER3 will be aggregated at the CBSA level. For asthma, the variable for lifetime asthma prevalence _LTASTH1 will be aggregated at the CBSA level. More information about the smoking and asthma variables available in the SMART BRFSS dataset is provided in Section 7.
There are several limitations of the project. One clear limitation is that the analyses will be restricted to areas of the US which are included in a CBSA grouping included in the SMART BRFSS dataset.
3 Methods
3.1 SMART BRFSS CDC dataset
# MMSA2021.xpt retrieved from https://www.cdc.gov/brfss/smart/smart_2021.html
# Accompanying documentation available at https://www.cdc.gov/brfss/annual_data/2021/pdf/2021_smart_brfss_mmsa_methodology-508.pdf
MMSA2021 <- read_xpt("data/input_data/mmsa/MMSA2021_XPT/MMSA2021.xpt") |>
mutate(`_resp_id` = str_pad(row_number(), pad = 0, width = 6)) # add respondent idMMSA2021 |> glimpse()# construct table of variable names and labels
mmsa_colname_labels <- MMSA2021 %>%
map_dfc(attr, "label") |>
pivot_longer(everything(),
names_to = "colname",
values_to = "label")
# include variable names without labels
mmsa_colnames_all <- MMSA2021 |>
colnames() |>
as_tibble() |>
rename(colname = value) |>
left_join(mmsa_colname_labels, by = join_by(colname))
# add missing labels documented in https://www.cdc.gov/brfss/annual_data/2021/pdf/2021_smart_brfss_mmsa_methodology-508.pdf
attr(MMSA2021[["_MMSA"]], "label") <- toupper("the code of metropolitan or micropolitan statistical area where the respondent lives")
attr(MMSA2021[["MMSANAME"]], "label") <- toupper("the MMSA name")
attr(MMSA2021[["_MMSAWT"]], "label") <- toupper("the MMSA-level weight that is used when generating MMSA-level estimates for variables in the data set")
attr(MMSA2021[["_resp_id"]], "label") <- toupper("respondent ID")
mmsa_colnames_all <- MMSA2021 %>%
map_dfc(attr, "label") |>
pivot_longer(everything(),
names_to = "colname",
values_to = "label")# rename columns to make them easier to work with using R (not requiring ``)
# underscore prefix indicates derived or computed, replace with d_
smart_2021 <- MMSA2021 |>
rename_with(~ tolower(str_replace(.x, "^_", "d_")))
smart_2021 |> colnames() [1] "dispcode" "statere1" "celphon1" "ladult1" "colgsex" "landsex"
[7] "respslct" "safetime" "cadult1" "cellsex" "hhadult" "sexvar"
[13] "genhlth" "physhlth" "menthlth" "poorhlth" "priminsr" "persdoc3"
[19] "medcost1" "checkup1" "exerany2" "bphigh6" "bpmeds" "cholchk3"
[25] "toldhi3" "cholmed3" "cvdinfr4" "cvdcrhd4" "cvdstrk3" "asthma3"
[31] "asthnow" "chcscncr" "chcocncr" "chccopd3" "addepev3" "chckdny2"
[37] "diabete4" "diabage3" "havarth5" "arthexer" "arthedu" "lmtjoin3"
[43] "arthdis2" "joinpai2" "marital" "educa" "renthom1" "numhhol3"
[49] "numphon3" "cpdemo1b" "veteran3" "employ1" "children" "income3"
[55] "pregnant" "weight2" "height3" "deaf" "blind" "decide"
[61] "diffwalk" "diffdres" "diffalon" "smoke100" "smokday2" "usenow3"
[67] "ecignow1" "alcday5" "avedrnk3" "drnk3ge5" "maxdrnks" "flushot7"
[73] "flshtmy3" "imfvpla2" "pneuvac4" "hivtst7" "hivtstd3" "fruit2"
[79] "fruitju2" "fvgreen1" "frenchf1" "potatoe1" "vegetab2" "d_ststr"
[85] "d_impsex" "cageg" "d_rfhlth" "d_phys14d" "d_ment14d" "d_hlthpln"
[91] "d_hcvu652" "d_totinda" "d_rfhype6" "d_cholch3" "d_rfchol3" "d_michd"
[97] "d_ltasth1" "d_casthm1" "d_asthms1" "d_drdxar3" "d_lmtact3" "d_lmtwrk3"
[103] "d_prace1" "d_mrace1" "d_hispanc" "d_race" "d_raceg21" "d_racegr3"
[109] "d_raceprv" "d_sex" "d_ageg5yr" "d_age65yr" "d_age80" "d_age_g"
[115] "wtkg3" "d_bmi5" "d_bmi5cat" "d_rfbmi5" "d_educag" "d_incomg1"
[121] "d_smoker3" "d_rfsmok3" "d_cureci1" "drnkany5" "d_rfbing5" "d_drnkwk1"
[127] "d_rfdrhv7" "d_flshot7" "d_pneumo3" "d_aidtst4" "ftjuda2_" "frutda2_"
[133] "grenda1_" "frnchda_" "potada1_" "vegeda2_" "d_misfrt1" "d_misveg1"
[139] "d_frtres1" "d_vegres1" "d_frutsu1" "d_vegesu1" "d_frtlt1a" "d_veglt1a"
[145] "d_frt16a" "d_veg23a" "d_fruite1" "d_vegete1" "d_mmsa" "d_mmsawt"
[151] "seqno" "mmsaname" "d_resp_id"
smart_2021 |> glimpse()# https://www.cdc.gov/brfss/annual_data/2021/pdf/2021_smart_brfss_mmsa_methodology-508.pdf
# Typically, BRFSS data are used to produce state-level estimates; however, for the SMART
# project, BRFSS data were used to produce small area-level estimates for MMSAs as defined by
# the US Census Bureau. On June 6, 2003, OMB issued new definitions for MMSA and
# metropolitan divisions. OMB periodically updates the list of MMSAs. The list of areas used for this
# analysis can be found here: https://www.census.gov/geographies/reference-files/timeseries/demo/metro-micro/delineation-files.html. The March 2020 release was used for defining
# the MMSAs in the 2021 SMART data set.
# retrieved from https://www.census.gov/geographies/reference-files/time-series/demo/metro-micro/historical-delineation-files.html
delin_2020 <- read_xls("data/input_data/mmsa/list1_2020.xls",
range = "A3:L1919") |>
rename_with(~ tolower(gsub(" ", "_", .x, fixed = TRUE))) |>
rename_with(~ tolower(gsub("/", "_", .x, fixed = TRUE)))
delin_2020_cbsa_join <- delin_2020 |> filter(is.na(metropolitan_division_code)) # TODO explore why this is necessary
delin_2020_mdc_join <- delin_2020 |> filter(!is.na(metropolitan_division_code)) # TODO explore why this is necessary
# TODO check this
mmsa_county_mapping_cbsa <- smart_2021 |>
mutate(d_mmsa_char = as.character(d_mmsa)) |>
distinct(d_mmsa, d_mmsa_char, d_mmsa_char, mmsaname) |>
inner_join(delin_2020_cbsa_join, by = c("d_mmsa_char" = "cbsa_code")) |>
distinct(d_mmsa, d_mmsa_char, csa_title, county_county_equivalent, fips_state_code, fips_county_code)
mmsa_county_mapping_mdc <- smart_2021 |>
mutate(d_mmsa_char = as.character(d_mmsa)) |>
distinct(d_mmsa, d_mmsa_char, d_mmsa_char, mmsaname) |>
inner_join(delin_2020_mdc_join, by = c("d_mmsa_char" = "metropolitan_division_code")) |>
distinct(d_mmsa, d_mmsa_char, csa_title, county_county_equivalent, fips_state_code, fips_county_code)
mmsa_county_mapping <- mmsa_county_mapping_cbsa |>
dplyr::union(mmsa_county_mapping_mdc) |>
distinct()smart_2021 |> summarize(n_distinct(d_mmsa)) |> pull() == mmsa_county_mapping |> summarize(n_distinct(d_mmsa)) |> pull()
# check for issues with leading zeros
smart_2021 |>
filter(nchar(d_mmsa) != 5)
smart_2021 |>
mutate(d_mmsa_char = as.character(d_mmsa)) |>
filter(nchar(d_mmsa_char) != 5)
mmsa_county_mapping |>
filter(nchar(d_mmsa_char) != 5)
delin_2020 |>
filter(nchar(cbsa_code) != 5)tigris_counties <- counties()
mmsa_county_mapping_geoid <- mmsa_county_mapping |>
mutate(smart = TRUE) |>
inner_join(tigris_counties, by = c("fips_state_code"= "STATEFP", "fips_county_code" = "COUNTYFP")) |>
mutate(smart = replace_na(smart, FALSE))
mmsa_county_mapping_geoid |> filter(smart == TRUE) |> nrow() == mmsa_county_mapping |> nrow()[1] TRUE
mmsa_county_mapping_geoid_sf <- mmsa_county_mapping_geoid |>
filter(!fips_state_code %in% c("02",
"15", "60",
"66", "69",
"72", "78")) |> # limit to contiguous states and DC
st_as_sf()
# simple map
# mmsa_county_mapping_geoid_sf |>
# st_simplify(dTolerance = 1e3) |>
# ggplot(aes(fill = smart)) +
# geom_sf()# Before we create the leaflet maps, we will change the CRS (Coordinate Reference System) to 4326 because leaflet expects the data provided to it to be in WGS84, which is EPSG:4326. We can do the transformation with st_transform.
# This is slow so I did it once and saved the RDS
mmsa_county_mapping_geoid_sf_4326 <- st_transform(mmsa_county_mapping_geoid_sf, crs = 4326)
mmsa_county_mapping_geoid_sf_4326 |> saveRDS("data/intermediate_data/mmsa_county_mapping_geoid_sf_4326.rds")mmsa_county_mapping_geoid_sf_4326 <- readRDS("data/intermediate_data/mmsa_county_mapping_geoid_sf_4326.rds")pal_fun <- colorFactor(topo.colors(2), mmsa_county_mapping_geoid_sf_4326$smart)
mmsa_county_mapping_geoid_sf_4326 |>
st_simplify(dTolerance = 1e3) |>
leaflet() |>
addPolygons(
stroke = FALSE,
# remove polygon borders
fillColor = ~ pal_fun(smart),
fillOpacity = 0.5,
smoothFactor = 0.5,
# increase opacity and resolution
popup = mmsa_county_mapping_geoid_sf_4326$county_county_equivalent
) |>
addProviderTiles(providers$CartoDB.Voyager) |> # add third party provider tile
addLegend(
"bottomright",
# location of legend
pal = pal_fun,
# palette function
values = ~ smart,
# variable to pass to palette function
title = 'Included in SMART BRFSS dataset',
# legend title
opacity = 1 # legend opacity (1 = completely opaque)
) |>
addScaleBar()3.1.1 Aggregating smoking and asthma variables at CBSA level
smart_2021 <- smart_2021 |>
mutate(
d_ever_smoker = if_else(d_smoker3 %in% c(1, 2, 3), 1, 0),
d_ever_asthma = if_else(d_ltasth1 == 2, 1, 0)
)3.1.1.1 _SMOKER3
# https://www.r-bloggers.com/2020/02/dem-7283-example-1-survey-statistics-using-brfss-data/
library(survey)
library(srvyr)
# https://stackoverflow.com/questions/55975478/problems-due-to-having-too-many-single-psus-at-stage-one
options(survey.adjust.domain.lonely=TRUE)
options(survey.lonely.psu="adjust")
# TODO check this
smart_2021_surv <- smart_2021 %>%
as_survey_design(ids = 1,
strata = d_ststr,
weights = d_mmsawt)# Investigate survey weighting use wtkg3 as can apply general expectations about distributions
# WEIGHT IN KG
# [2 implied decimal places]
smart_2021 %>%
filter(!is.na(d_mmsawt)) |>
ggplot(aes(x = wtkg3 / 100)) + # divide by 100 for 2 implied decimal places
geom_density() +
theme_minimal()
uw_wtkg3_mmsa_tbl <- smart_2021 %>%
filter(!is.na(d_mmsawt)) |>
group_by(d_mmsa, mmsaname) |>
summarize(uw_wtkg3 = mean(wtkg3, na.rm = TRUE))
w_wtkg3_mmsa_tbl <- svyby( ~ wtkg3,
~ d_mmsa,
design = smart_2021_surv,
FUN = svymean,
na.rm = TRUE) |>
rename(w_wtkg3 = wtkg3)
# TODO investigate "only one PSU at stage 1" warnings / confirm approach
wtkg3_mmsa_tbl <- w_wtkg3_mmsa_tbl |>
inner_join(uw_wtkg3_mmsa_tbl, by = "d_mmsa") |>
select(-se)
wtkg3_mmsa_tbl |>
pivot_longer(cols = c("w_wtkg3", "uw_wtkg3"),
values_to = "wtkg3") |>
ggplot(aes(x = wtkg3 / 100, colour = name)) + # divide by 100 for 2 implied decimal places
geom_density() +
theme_minimal()
wtkg3_mmsa_tbl |>
arrange(desc(w_wtkg3)) |>
head(5)
wtkg3_mmsa_tbl |>
arrange(w_wtkg3) |>
head(5)
pal_fun_weight <- colorNumeric("BuPu", NULL)
mmsa_county_mapping_geoid_sf_4326 |>
left_join(wtkg3_mmsa_tbl, by = "d_mmsa") |>
mutate(weight_kg = w_wtkg3/100) |>
st_simplify(dTolerance = 1e3) |>
leaflet() |>
addPolygons(
stroke = FALSE,
# remove polygon borders
fillColor = ~ pal_fun_weight(weight_kg),
fillOpacity = 0.5,
smoothFactor = 0.5,
# increase opacity and resolution
popup = mmsa_county_mapping_geoid_sf_4326$county_county_equivalent
) |>
addProviderTiles(providers$CartoDB.Voyager) |> # add third party provider tile
addLegend(
"bottomright",
# location of legend
pal = pal_fun_weight,
# palette function
values = ~ weight_kg,
# variable to pass to palette function
title = 'Mean weight (kg)',
# legend title
opacity = 1 # legend opacity (1 = completely opaque)
) |>
addScaleBar()# TODO make function
uw_ever_smoker_mmsa_tbl <- smart_2021 %>%
group_by(d_mmsa, mmsaname) |>
summarize(uw_ever_smoker = mean(d_ever_smoker))
w_ever_smoker_mmsa_tbl <- svyby(
~ d_ever_smoker,
~ d_mmsa,
design = smart_2021_surv,
FUN = svymean,
na.rm = TRUE) |>
rename(w_ever_smoker = d_ever_smoker)
ever_smoker_mmsa_tbl <- w_ever_smoker_mmsa_tbl |>
inner_join(uw_ever_smoker_mmsa_tbl, by = "d_mmsa") |>
select(-se)ever_smoker_mmsa_tbl |>
arrange(desc(w_ever_smoker)) |>
head(5)
ever_smoker_mmsa_tbl |>
arrange(w_ever_smoker) |>
head(5)pal_fun_smoker <- colorNumeric("inferno", NULL)
mmsa_county_mapping_geoid_sf_4326 |>
left_join(ever_smoker_mmsa_tbl, by = "d_mmsa") |>
st_simplify(dTolerance = 1e3) |>
leaflet() |>
addPolygons(
stroke = FALSE,
# remove polygon borders
fillColor = ~ pal_fun_smoker(w_ever_smoker),
fillOpacity = 0.5,
smoothFactor = 0.5,
# increase opacity and resolution
popup = mmsa_county_mapping_geoid_sf_4326$county_county_equivalent
) |>
addProviderTiles(providers$CartoDB.Voyager) |> # add third party provider tile
addLegend(
"bottomright",
# location of legend
pal = pal_fun_smoker,
# palette function
values = ~ w_ever_smoker,
# variable to pass to palette function
title = 'Proportion smoker',
# legend title
opacity = 1 # legend opacity (1 = completely opaque)
) |>
addScaleBar()3.1.1.2 _LTASTH1
# TODO make function
uw_ever_asthma_mmsa_tbl <- smart_2021 %>%
group_by(d_mmsa, mmsaname) |>
summarize(uw_ever_asthma = mean(d_ever_asthma))
w_ever_asthma_mmsa_tbl <- svyby(
~ d_ever_asthma,
~ d_mmsa,
design = smart_2021_surv,
FUN = svymean,
na.rm = TRUE) |>
rename(w_ever_asthma = d_ever_asthma)
ever_asthma_mmsa_tbl <- w_ever_asthma_mmsa_tbl |>
inner_join(uw_ever_asthma_mmsa_tbl, by = "d_mmsa") |>
select(-se)ever_asthma_mmsa_tbl |>
arrange(desc(w_ever_asthma)) |>
head(5)
ever_asthma_mmsa_tbl |>
arrange(w_ever_asthma) |>
head(5)pal_fun_asthma <- colorNumeric("magma", NULL)
mmsa_county_mapping_geoid_sf_4326 |>
left_join(ever_asthma_mmsa_tbl, by = "d_mmsa") |>
st_simplify(dTolerance = 1e3) |>
leaflet() |>
addPolygons(
stroke = FALSE,
# remove polygon borders
fillColor = ~ pal_fun_asthma(w_ever_asthma),
fillOpacity = 0.5,
smoothFactor = 0.5,
# increase opacity and resolution
popup = mmsa_county_mapping_geoid_sf_4326$county_county_equivalent
) |>
addProviderTiles(providers$CartoDB.Voyager) |> # add third party provider tile
addLegend(
"bottomright",
# location of legend
pal = pal_fun_asthma,
# palette function
values = ~ w_ever_asthma,
# variable to pass to palette function
title = 'Proportion asthma',
# legend title
opacity = 1 # legend opacity (1 = completely opaque)
) |>
addScaleBar()3.2 SVI dataset
# retrieved from https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html
# documentation here https://www.atsdr.cdc.gov/placeandhealth/svi/documentation/pdf/SVI2020Documentation_08.05.22.pdf
svi2020 <- read_csv("data/input_data/svi/SVI_2020_US_county.csv")svi2020 |> glimpse()svi2020_sf <- svi2020 |>
select(RPL_THEME1,
RPL_THEME2,
RPL_THEME3,
RPL_THEME4,
RPL_THEMES,
FIPS) |>
left_join(tigris_counties, by = c("FIPS" = "GEOID"))
st_as_sf()
svi2020_sf_4326 <- st_transform(svi2020_sf, crs = 4326)
svi2020_sf_4326 |> saveRDS("data/intermediate_data/svi2020_sf_4326.rds")svi2020_sf_4326 <-
readRDS("data/intermediate_data/svi2020_sf_4326.rds") |>
filter(!STATEFP %in% c("02",
"15", "60",
"66", "69",
"72", "78")) # limit to contiguous states and DCpal_svi <- colorNumeric("inferno", NULL, reverse = TRUE)
svi2020_sf_4326 |>
filter(!is.na(FUNCSTAT)) |> # TODO explore why this is necessary
st_simplify(dTolerance = 1e3) |>
leaflet() |>
addPolygons(
stroke = FALSE,
# remove polygon borders
fillColor = ~ pal_svi(RPL_THEME1),
fillOpacity = 0.5,
smoothFactor = 0.5
# increase opacity and resolution
) |>
addProviderTiles(providers$CartoDB.Voyager) |> # add third party provider tile
addLegend(
"bottomright",
# location of legend
pal = pal_svi,
# palette function
values = ~ RPL_THEMES,
# variable to pass to palette function
title = 'SVI',
# legend title
opacity = 1 # legend opacity (1 = completely opaque)
) |>
addScaleBar()3.3 ADI dataset
# retrieved from https://www.neighborhoodatlas.medicine.wisc.edu/
adi2021 <- read_csv("data/input_data/adi/adi-download/US_2021_ADI_Census_Block_Group_v4_0_1.csv")
tigris_cbg <- block_groups(cb = TRUE) |>
filter(!STATEFP %in% c("02",
"15", "60",
"66", "69",
"72", "78"))adi2021 |> glimpse()adi2021_sf <- adi2021 |>
inner_join(tigris_cbg, by = c("FIPS" = "GEOID")) |>
st_as_sf()
adi2021_cbg_sf_4326 <- st_transform(adi2021_sf, crs = 4326)
adi2021_cbg_sf_4326 |> saveRDS("data/intermediate_data/adi2021_cbg_sf_4326.rds")adi_no_cbg <- adi2021 |>
anti_join(tigris_cbg, by = c("FIPS" = "GEOID"))
cbg_no_adi <- tigris_cbg |>
anti_join(adi2021, by = c("GEOID" = "FIPS"))
# When a Census block group falls into one or more of the suppression criteria mentioned above the ADI rank is replaced with a code describing the suppression reason. Three possible codes will appear in the ADI field: "PH" for suppression due to low population and/or housing, "GQ" for suppression due to a high group quarters population, and "PH-GQ" for suppression due to both types of suppression criteria. A code of "QDI" designates block groups without an ADI due to Questionable Data Integrity, stemming from missing data in the source ACS data.contig_state_vctr <- datasets::state.abb |>
setdiff(c("AK", "HI"))
# https://walker-data.com/umich-workshop-2022/intro-2020-census/#1
# get cbg counts to compute cbg weights for aggregating adi at county level
cbg_counts <- list()
for (contig_state in contig_state_vctr) {
cbg_counts[[contig_state]] <- get_decennial(
geography = "block group",
variables = "P1_001N",
state = contig_state,
year = 2020,
output = "wide"
) |>
mutate(state_abb = contig_state)
}
cbg_counts |> saveRDS("data/intermediate_data/cbg_counts.RDS")cbg_counts <- readRDS("data/intermediate_data/cbg_counts.RDS") |>
bind_rows()
# computed cbg weights for county level
cbg_wts <- tigris_cbg |>
select(STATEFP, COUNTYFP, TRACTCE, BLKGRPCE, GEOID) |>
st_drop_geometry() |>
inner_join(cbg_counts, by = "GEOID") |>
group_by(STATEFP, COUNTYFP) |>
# county pop
mutate(county_pop = sum(P1_001N)) |>
ungroup() |>
# cbg pop / county pop
mutate(cbg_wt = P1_001N/county_pop) |>
arrange(GEOID)
cbg_wts |> saveRDS("data/intermediate_data/cbg_wts.RDS")cbg_wts <- readRDS("data/intermediate_data/cbg_wts.RDS")
# compute weighted average for adi at county level
county_adi <- adi2021_cbg_sf_4326 |>
st_drop_geometry() |>
select(-STATEFP, -COUNTYFP, -TRACTCE, -BLKGRPCE, -NAME) |>
inner_join(cbg_wts, by = c("FIPS" = "GEOID")) |>
mutate(ADI_NATRANK = as.numeric(ADI_NATRANK)) |>
filter(!is.na(ADI_NATRANK)) |>
# national adi rank * cbg wt
mutate(adi_wts = ADI_NATRANK * cbg_wt) |>
group_by(STATEFP, COUNTYFP) |>
# weighted average sum of weighted adis
summarize(county_adi_value = sum(adi_wts))
county_adi |> saveRDS("data/intermediate_data/county_adi.RDS")
# cbg_counts_comb <- cbg_counts |>
# bind_rows()
# cbg_counts_comb |> ggplot(aes(x = P1_001N)) + geom_histogram()county_adi <- readRDS("data/intermediate_data/county_adi.RDS") |>
mutate(FIPS = paste0(STATEFP, COUNTYFP)) |>
left_join(tigris_counties, by = c("FIPS" = "GEOID")) |>
st_as_sf()
adi2021_county_sf_4326 <- st_transform(county_adi, crs = 4326)
adi2021_county_sf_4326 |> saveRDS("data/intermediate_data/adi2021_county_sf_4326.rds")adi2021_county_sf_4326 <- readRDS("data/intermediate_data/adi2021_county_sf_4326.rds")
pal_adi <- colorNumeric("magma", NULL)
adi2021_county_sf_4326 |>
st_simplify(dTolerance = 1e3) |>
leaflet() |>
addPolygons(
stroke = FALSE,
# remove polygon borders
fillColor = ~ pal_adi(county_adi_value),
fillOpacity = 0.5,
smoothFactor = 0.5
# increase opacity and resolution
) |>
addProviderTiles(providers$CartoDB.Voyager) |> # add third party provider tile
addLegend(
"bottomright",
# location of legend
pal = pal_adi,
# palette function
values = ~ county_adi_value,
# variable to pass to palette function
title = 'adi',
# legend title
opacity = 1 # legend opacity (1 = completely opaque)
) |>
addScaleBar()3.4 Combining datasets
combined_dataset <- mmsa_county_mapping_geoid_sf_4326 |>
select(d_mmsa, county_county_equivalent, fips_state_code, fips_county_code) |>
left_join(select(ever_asthma_mmsa_tbl, -mmsaname), by = "d_mmsa") |>
left_join(select(ever_smoker_mmsa_tbl, -mmsaname), by = "d_mmsa") |>
st_join(select(svi2020_sf_4326, starts_with("RPL")),
left = TRUE,
join = st_equals) |>
st_join(select(adi2021_county_sf_4326, county_adi_value),
left = TRUE,
join = st_equals)
combined_dataset |> saveRDS("data/final_data/combined_dataset.rds")# add county counts for poisson regression
combined_dataset <- readRDS("data/final_data/combined_dataset.rds")
combined_dataset_no_geo <- combined_dataset |>
st_drop_geometry()
county_cts <- get_decennial(
geography = "county",
variables = "P1_001N",
year = 2020,
output = "wide"
) |>
rename(county_ct = P1_001N)
combined_dataset_w_counts <- combined_dataset_no_geo |>
mutate(GEOID = paste0(fips_state_code, fips_county_code)) |>
left_join(county_cts, by = "GEOID") |>
mutate(
n_w_ever_smoker = floor(w_ever_smoker * county_ct),
n_uw_ever_smoker = floor(uw_ever_smoker * county_ct),
n_w_ever_asthma = floor(w_ever_asthma * county_ct),
n_uw_ever_asthma = floor(uw_ever_asthma * county_ct)
)
combined_dataset_w_counts |> saveRDS("data/final_data/combined_dataset_w_counts.rds")4 Results
4.1 Analyses
combined_dataset_w_counts <- readRDS("data/final_data/combined_dataset_w_counts.rds")
# benefited from approach here https://rpubs.com/cqj_00/785193
combined_corrs <- combined_dataset_w_counts |>
select(
w_ever_asthma,
w_ever_smoker,
RPL_THEME1,
RPL_THEME2,
RPL_THEME3,
RPL_THEME4,
RPL_THEMES,
county_adi_value,
county_ct
) |>
cor(use = "complete.obs", method = "spearman") |>
as.matrix() %>%
get_tri() %>%
reshape2::melt(na.rm = TRUE) |>
rename(variable_1 = Var1,
variable_2 = Var2)
combined_corrs |>
ggplot(aes(x = variable_2, y = variable_1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(
low = "blue",
high = "red",
mid = "grey100",
# midpoint = 0.5,
limit = c(-1, 0.999),
space = "Lab",
name = "Spearman\nCorrelation"
) +
theme_minimal() + # minimal theme
theme(
axis.text.x = element_text(
angle = 45,
vjust = 1,
size = 10,
hjust = 1
),
axis.text.y = element_text(size = 10)
) +
coord_fixed() +
coord_flip()4.1.1 Correlations between SVI and ADI
svi_adi_model <-
glm(county_adi_value ~ RPL_THEME1 + RPL_THEME2 + RPL_THEME3 + RPL_THEME4 + county_ct,
family = gaussian(),
data = combined_dataset_w_counts)
modelsummary(
list(
"svi_adi_model" = svi_adi_model
),
coef_omit = "Intercept",
statistic = c("p = {p.value}"),
estimate = "{estimate} [{conf.low}, {conf.high}]"
)| svi_adi_model | |
|---|---|
| RPL_THEME1 | 35.498 [30.280, 40.717] |
| p = <0.001 | |
| RPL_THEME2 | 25.911 [21.213, 30.609] |
| p = <0.001 | |
| RPL_THEME3 | −40.294 [−44.688, −35.899] |
| p = <0.001 | |
| RPL_THEME4 | 4.416 [0.308, 8.525] |
| p = 0.035 | |
| county_ct | 0.000 [0.000, 0.000] |
| p = <0.001 | |
| Num.Obs. | 636 |
| R2 | 0.606 |
| AIC | 4917.1 |
| BIC | 4948.3 |
| Log.Lik. | −2451.563 |
| RMSE | 11.42 |
4.1.2 Smoking
combined_dataset_w_counts |>
ggplot(aes(x = w_ever_smoker)) +
geom_density() +
theme_minimal()combined_dataset_w_counts |>
ggplot(aes(x = w_ever_smoker,
y = county_adi_value)) +
geom_point(alpha = 0.5) +
theme_minimal()combined_dataset_w_counts |>
ggplot(aes(x = w_ever_smoker,
y = RPL_THEME3)) +
geom_point(alpha = 0.5) +
theme_minimal()# remove outliers
smoking_dataset_cleaned <- combined_dataset_w_counts |>
filter(w_ever_smoker >= 0.2)
# exploring modelling approaches..
# benefitted from https://rpubs.com/kaz_yos/poisson
smoking_quasibinom_adi_only <-
glm(w_ever_smoker ~ county_adi_value,
family = quasibinomial(),
data = smoking_dataset_cleaned)
smoking_quasipoisson_adi_only <-
glm(
n_w_ever_smoker ~ county_adi_value,
offset = log(county_ct),
family = quasipoisson(link = "log"),
data = smoking_dataset_cleaned
)
smoking_quasibinom <-
glm(w_ever_smoker ~ county_adi_value * RPL_THEME3,
family = quasibinomial(),
data = smoking_dataset_cleaned)
smoking_quasipoisson <-
glm(
n_w_ever_smoker ~ county_adi_value * RPL_THEME3,
offset = log(county_ct),
family = quasipoisson(link = "log"),
data = smoking_dataset_cleaned
)
modelsummary(
list(
"smoking_quasibinom" = smoking_quasibinom,
"smoking_quasibinom_adi_only" = smoking_quasibinom_adi_only,
"smoking_quasipoisson" = smoking_quasipoisson,
"smoking_quasipoisson_adi_only" = smoking_quasipoisson_adi_only
),
coef_omit = "Intercept",
statistic = c("p = {p.value}"),
estimate = "{estimate} [{conf.low}, {conf.high}]",
exponentiate = TRUE
)| smoking_quasibinom | smoking_quasibinom_adi_only | smoking_quasipoisson | smoking_quasipoisson_adi_only | |
|---|---|---|---|---|
| county_adi_value | 1.004 [1.002, 1.006] | 1.006 [1.006, 1.007] | 1.001 [1.000, 1.003] | 1.005 [1.004, 1.005] |
| p = <0.001 | p = <0.001 | p = 0.096 | p = <0.001 | |
| RPL_THEME3 | 0.645 [0.545, 0.764] | 0.623 [0.556, 0.698] | ||
| p = <0.001 | p = <0.001 | |||
| county_adi_value × RPL_THEME3 | 1.003 [1.000, 1.006] | 1.004 [1.001, 1.006] | ||
| p = 0.053 | p = 0.001 | |||
| Num.Obs. | 634 | 634 | 634 | 634 |
| RMSE | 0.04 | 0.04 | 17935.23 | 22668.48 |
4.1.3 Asthma
combined_dataset_w_counts |>
ggplot(aes(x = w_ever_asthma)) +
geom_density() +
theme_minimal()combined_dataset_w_counts |>
ggplot(aes(x = w_ever_asthma,
y = county_adi_value)) +
geom_point(alpha = 0.5) +
theme_minimal()combined_dataset_w_counts |>
ggplot(aes(x = w_ever_asthma,
y = RPL_THEME3)) +
geom_point(alpha = 0.5) +
theme_minimal()# remove outliers
asthma_dataset_cleaned <- combined_dataset_w_counts
# exploring modelling approaches..
# benefitted from https://rpubs.com/kaz_yos/poisson
asthma_quasibinom <-
glm(w_ever_asthma ~ county_adi_value * RPL_THEME3,
family = quasibinomial(),
data = asthma_dataset_cleaned)
asthma_quasipoisson <-
glm(
n_w_ever_asthma ~ county_adi_value * RPL_THEME3,
offset = log(county_ct),
family = quasipoisson(link = "log"),
data = asthma_dataset_cleaned
)
asthma_quasibinom_adi_only <-
glm(w_ever_asthma ~ county_adi_value,
family = quasibinomial(),
data = asthma_dataset_cleaned)
asthma_quasipoisson_adi_only <-
glm(
n_w_ever_asthma ~ county_adi_value,
offset = log(county_ct),
family = quasipoisson(link = "log"),
data = asthma_dataset_cleaned
)
modelsummary(
list(
"asthma_quasibinom" = asthma_quasibinom,
"asthma_quasibinom_adi_only" = asthma_quasibinom_adi_only,
"asthma_quasipoisson" = asthma_quasipoisson,
"asthma_quasipoisson_adi_only" = asthma_quasipoisson_adi_only
),
coef_omit = "Intercept",
statistic = c("p = {p.value}"),
estimate = "{estimate} [{conf.low}, {conf.high}]",
exponentiate = TRUE
)| asthma_quasibinom | asthma_quasibinom_adi_only | asthma_quasipoisson | asthma_quasipoisson_adi_only | |
|---|---|---|---|---|
| county_adi_value | 1.000 [0.999, 1.002] | 1.000 [1.000, 1.001] | 0.999 [0.997, 1.001] | 1.001 [1.000, 1.001] |
| p = 0.923 | p = 0.254 | p = 0.210 | p = 0.028 | |
| RPL_THEME3 | 0.894 [0.775, 1.030] | 0.736 [0.644, 0.842] | ||
| p = 0.120 | p = <0.001 | |||
| county_adi_value × RPL_THEME3 | 1.000 [0.998, 1.002] | 1.002 [0.999, 1.005] | ||
| p = 0.917 | p = 0.154 | |||
| Num.Obs. | 636 | 636 | 636 | 636 |
| RMSE | 0.02 | 0.02 | 9832.47 | 11178.78 |
5 Conclusion
6 References
Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Questionnaire. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2021.
Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2021.
Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research, Analysis, and Services Program. CDC/ATSDR Social Vulnerability Index 2020 Database US. https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html. Accessed on 2023/11/04.
Kind AJH, Buckingham W. Making Neighborhood Disadvantage Metrics Accessible: The Neighborhood Atlas. New England Journal of Medicine, 2018. 378: 2456-2458. DOI: 10.1056/NEJMp1802313. PMCID: PMC6051533.
Rollings KA, Noppert GA, Griggs JJ, Melendez RA, Clarke PJ. Comparison of two area-level socioeconomic deprivation indices: Implications for public health research, practice, and policy. PLoS One. 2023 Oct 5;18(10):e0292281. doi: 10.1371/journal.pone.0292281. PMID: 37797080; PMCID: PMC10553799.
Tipirneni R, Schmidt H, Lantz PM, Karmakar M. Associations of 4 Geographic Social Vulnerability Indices With US COVID-19 Incidence and Mortality. Am J Public Health. 2022 Nov;112(11):1584-1588. doi: 10.2105/AJPH.2022.307018. Epub 2022 Sep 15. PMID: 36108250; PMCID: PMC9558191.
University of Wisconsin School of Medicine and Public Health. 2021 Area Deprivation Index v4.0.1. Downloaded from https://www.neighborhoodatlas.medicine.wisc.edu/ 2023/11/04
Xie S, Himes BE. Approaches to Link Geospatially Varying Social, Economic, and Environmental Factors with Electronic Health Record Data to Better Understand Asthma Exacerbations. AMIA Annu Symp Proc. 2018 Dec 5;2018:1561-1570. PMID: 30815202; PMCID: PMC6371292.
Xie S, Hubbard RA, Himes BE. Neighborhood-level measures of socioeconomic status are more correlated with individual-level measures in urban areas compared with less urban areas. Ann Epidemiol. 2020 Mar;43:37-43.e4. doi: 10.1016/j.annepidem.2020.01.012. Epub 2020 Feb 11. PMID: 32151518; PMCID: PMC7160852.
7 Appendices
7.1 Session Info
sessionInfo()R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22621)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] srvyr_1.2.0 survey_4.2-1 survival_3.5-5
[4] Matrix_1.6-1.1 modelsummary_1.4.2 broom.mixed_0.2.9.4
[7] kableExtra_1.3.4 RColorBrewer_1.1-3 gridExtra_2.3
[10] leaflet_2.2.0 sf_1.0-14 tidycensus_1.5
[13] tigris_2.0.4 readxl_1.4.3 lubridate_1.9.2
[16] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
[19] purrr_1.0.2 readr_2.1.4 tidyr_1.3.0
[22] tibble_3.2.1 ggplot2_3.4.3 tidyverse_2.0.0
[25] haven_2.5.3
loaded via a namespace (and not attached):
[1] DBI_1.1.3 s2_1.1.4 rlang_1.1.1
[4] magrittr_2.0.3 furrr_0.3.1 e1071_1.7-13
[7] compiler_4.3.1 reshape2_1.4.4 systemfonts_1.0.4
[10] vctrs_0.6.3 rvest_1.0.3 pkgconfig_2.0.3
[13] wk_0.8.0 crayon_1.5.2 fastmap_1.1.1
[16] backports_1.4.1 ellipsis_0.3.2 labeling_0.4.3
[19] utf8_1.2.3 rmarkdown_2.24 tzdb_0.4.0
[22] bit_4.0.5 xfun_0.40 jsonlite_1.8.7
[25] uuid_1.1-1 broom_1.0.5 parallel_4.3.1
[28] R6_2.5.1 tables_0.9.17 stringi_1.7.12
[31] parallelly_1.36.0 estimability_1.4.1 jquerylib_0.1.4
[34] cellranger_1.1.0 Rcpp_1.0.11 knitr_1.43
[37] future.apply_1.11.0 parameters_0.21.2 leaflet.providers_2.0.0
[40] splines_4.3.1 timechange_0.2.0 tidyselect_1.2.0
[43] rstudioapi_0.15.0 yaml_2.3.7 viridis_0.6.4
[46] codetools_0.2-19 listenv_0.9.0 plyr_1.8.9
[49] lattice_0.21-8 bayestestR_0.13.1 withr_2.5.1
[52] coda_0.19-4 evaluate_0.21 future_1.33.0
[55] units_0.8-4 proxy_0.4-27 xml2_1.3.5
[58] pillar_1.9.0 KernSmooth_2.23-21 checkmate_2.2.0
[61] DT_0.30 insight_0.19.5 generics_0.1.3
[64] vroom_1.6.3 hms_1.1.3 munsell_0.5.0
[67] scales_1.2.1 xtable_1.8-4 globals_0.16.2
[70] class_7.3-22 glue_1.6.2 emmeans_1.8.8
[73] tools_4.3.1 webshot_0.5.5 mvtnorm_1.2-3
[76] mitools_2.4 crosstalk_1.2.0 datawizard_0.9.0
[79] colorspace_2.1-0 nlme_3.1-162 performance_0.10.5
[82] cli_3.6.1 rappdirs_0.3.3 fansi_1.0.4
[85] viridisLite_0.4.2 svglite_2.1.1 rematch_1.0.1
[88] gt_0.9.0 gtable_0.3.4 digest_0.6.33
[91] classInt_0.4-10 htmlwidgets_1.6.2 farver_2.1.1
[94] htmltools_0.5.6 lifecycle_1.0.3 httr_1.4.7
[97] MASS_7.3-60 bit64_4.0.5
7.2 SMART BRFSS CDC dataset
mmsa_colnames_all |>
kable()| colname | label |
|---|---|
| DISPCODE | FINAL DISPOSITION |
| STATERE1 | RESIDENT OF STATE |
| CELPHON1 | CELLULAR TELEPHONE |
| LADULT1 | ARE YOU 18 YEARS OF AGE OR OLDER? |
| COLGSEX | ARE YOU MALE OR FEMALE? |
| LANDSEX | ARE YOU MALE OR FEMALE? |
| RESPSLCT | RESPONDENT SELECTION |
| SAFETIME | SAFE TIME TO TALK? |
| CADULT1 | ARE YOU 18 YEARS OF AGE OR OLDER? |
| CELLSEX | ARE YOU MALE OR FEMALE? |
| HHADULT | NUMBER OF ADULTS IN HOUSEHOLD |
| SEXVAR | SEX OF RESPONDENT |
| GENHLTH | GENERAL HEALTH |
| PHYSHLTH | NUMBER OF DAYS PHYSICAL HEALTH NOT GOOD |
| MENTHLTH | NUMBER OF DAYS MENTAL HEALTH NOT GOOD |
| POORHLTH | POOR PHYSICAL OR MENTAL HEALTH |
| PRIMINSR | WHAT IS PRIMARY SOURCE OF HEALTH INSURAN |
| PERSDOC3 | HAVE PERSONAL HEALTH CARE PROVIDER? |
| MEDCOST1 | COULD NOT AFFORD TO SEE DOCTOR |
| CHECKUP1 | LENGTH OF TIME SINCE LAST ROUTINE CHECKU |
| EXERANY2 | EXERCISE IN PAST 30 DAYS |
| BPHIGH6 | EVER TOLD BLOOD PRESSURE HIGH |
| BPMEDS | CURRENTLY TAKING BLOOD PRESSURE MEDICATI |
| CHOLCHK3 | HOW LONG SINCE CHOLESTEROL CHECKED |
| TOLDHI3 | EVER TOLD CHOLESTEROL IS HIGH |
| CHOLMED3 | CURRENTLY TAKING MEDICINE FOR HIGH CHOLE |
| CVDINFR4 | EVER DIAGNOSED WITH HEART ATTACK |
| CVDCRHD4 | EVER DIAGNOSED WITH ANGINA OR CORONARY H |
| CVDSTRK3 | EVER DIAGNOSED WITH A STROKE |
| ASTHMA3 | EVER TOLD HAD ASTHMA |
| ASTHNOW | STILL HAVE ASTHMA |
| CHCSCNCR | (EVER TOLD) YOU HAD SKIN CANCER? |
| CHCOCNCR | (EVER TOLD) YOU HAD ANY OTHER TYPES OF C |
| CHCCOPD3 | EVER TOLD YOU HAD C.O.P.D. EMPHYSEMA OR |
| ADDEPEV3 | (EVER TOLD) YOU HAD A DEPRESSIVE DISORDE |
| CHCKDNY2 | EVER TOLD YOU HAVE KIDNEY DISEASE? |
| DIABETE4 | (EVER TOLD) YOU HAD DIABETES |
| DIABAGE3 | AGE WHEN TOLD DIABETES |
| HAVARTH5 | TOLD HAVE ARTHRITIS |
| ARTHEXER | DR. SUGGEST USE OF PHYSICAL ACTIVITY OR |
| ARTHEDU | EVER TAKEN CLASS IN MANAGING ARTHRITIS O |
| LMTJOIN3 | LIMITED BECAUSE OF JOINT SYMPTOMS |
| ARTHDIS2 | DOES ARTHRITIS AFFECT WHETHER YOU WORK |
| JOINPAI2 | HOW BAD WAS JOINT PAIN |
| MARITAL | MARITAL STATUS |
| EDUCA | EDUCATION LEVEL |
| RENTHOM1 | OWN OR RENT HOME |
| NUMHHOL3 | HOUSEHOLD TELEPHONES |
| NUMPHON3 | RESIDENTIAL PHONES |
| CPDEMO1B | DO YOU HAVE A CELL PHONE FOR PERSONAL US |
| VETERAN3 | ARE YOU A VETERAN |
| EMPLOY1 | EMPLOYMENT STATUS |
| CHILDREN | NUMBER OF CHILDREN IN HOUSEHOLD |
| INCOME3 | INCOME LEVEL |
| PREGNANT | PREGNANCY STATUS |
| WEIGHT2 | REPORTED WEIGHT IN POUNDS |
| HEIGHT3 | REPORTED HEIGHT IN FEET AND INCHES |
| DEAF | ARE YOU DEAF OR DO YOU HAVE SERIOUS DIFF |
| BLIND | BLIND OR DIFFICULTY SEEING |
| DECIDE | DIFFICULTY CONCENTRATING OR REMEMBERING |
| DIFFWALK | DIFFICULTY WALKING OR CLIMBING STAIRS |
| DIFFDRES | DIFFICULTY DRESSING OR BATHING |
| DIFFALON | DIFFICULTY DOING ERRANDS ALONE |
| SMOKE100 | SMOKED AT LEAST 100 CIGARETTES |
| SMOKDAY2 | FREQUENCY OF DAYS NOW SMOKING |
| USENOW3 | USE OF SMOKELESS TOBACCO PRODUCTS |
| ECIGNOW1 | DO YOU NOW USE E-CIGARETTES, EVERY DAY, |
| ALCDAY5 | DAYS IN PAST 30 HAD ALCOHOLIC BEVERAGE |
| AVEDRNK3 | AVG ALCOHOLIC DRINKS PER DAY IN PAST 30 |
| DRNK3GE5 | BINGE DRINKING |
| MAXDRNKS | MOST DRINKS ON SINGLE OCCASION PAST 30 D |
| FLUSHOT7 | ADULT FLU SHOT/SPRAY PAST 12 MOS |
| FLSHTMY3 | WHEN RECEIVED MOST RECENT SEASONAL FLU S |
| IMFVPLA2 | WHERE DID YOU GET YOUR LAST FLU SHOT/VAC |
| PNEUVAC4 | PNEUMONIA SHOT EVER |
| HIVTST7 | EVER TESTED H.I.V. |
| HIVTSTD3 | MONTH AND YEAR OF LAST HIV TEST |
| FRUIT2 | HOW MANY TIMES DID YOU EAT FRUIT? |
| FRUITJU2 | HOW MANY TIMES DID YOU DRINK 100 PERCENT |
| FVGREEN1 | HOW MANY TIMES DID YOU EAT DARK GREEN VE |
| FRENCHF1 | HOW OFTEN DO YOU EAT FRENCH FRIES OR FRI |
| POTATOE1 | HOW OFTEN DO YOU EAT FRENCH FRIES OR FRI |
| VEGETAB2 | HOW OFTEN DO YOU EAT FRENCH FRIES OR FRI |
| _STSTR | SAMPLE DESIGN STRATIFICATION VARIABLE |
| _IMPSEX | IMPUTED GENDER |
| CAGEG | FOUR LEVEL CHILD AGE |
| _RFHLTH | ADULTS WITH GOOD OR BETTER HEALTH |
| _PHYS14D | COMPUTED PHYSICAL HEALTH STATUS |
| _MENT14D | COMPUTED MENTAL HEALTH STATUS |
| _HLTHPLN | HAVE ANY HEALTH INSURANCE |
| _HCVU652 | RESPONDENTS AGED 18-64 WITH HEALTH INSUR |
| _TOTINDA | LEISURE TIME PHYSICAL ACTIVITY CALCULATE |
| _RFHYPE6 | HIGH BLOOD PRESSURE CALCULATED VARIABLE |
| _CHOLCH3 | CHOLESTEROL CHECKED CALCULATED VARIABLE |
| _RFCHOL3 | HIGH CHOLESTEROL CALCULATED VARIABLE |
| _MICHD | RESPONDENTS THAT HAVE EVER REPORTED HAVI |
| _LTASTH1 | LIFETIME ASTHMA CALCULATED VARIABLE |
| _CASTHM1 | CURRENT ASTHMA CALCULATED VARIABLE |
| _ASTHMS1 | COMPUTED ASTHMA STATUS |
| _DRDXAR3 | RESPONDENTS DIAGNOSED WITH ARTHRITIS |
| _LMTACT3 | LIMITED USUAL ACTIVITIES |
| _LMTWRK3 | LIMITED WORK ACTIVITIES |
| _PRACE1 | COMPUTED PREFERRED RACE |
| _MRACE1 | CALCULATED NON-HISPANIC RACE INCLUDING M |
| _HISPANC | HISPANIC, LATINO/A, OR SPANISH ORIGIN CA |
| _RACE | COMPUTED RACE-ETHNICITY GROUPING |
| _RACEG21 | COMPUTED NON-HISPANIC WHITES/ALL OTHERS |
| _RACEGR3 | COMPUTED FIVE LEVEL RACE/ETHNICITY CATEG |
| _RACEPRV | COMPUTED RACE GROUPS USED FOR INTERNET P |
| _SEX | CALCULATED SEX VARIABLE |
| _AGEG5YR | REPORTED AGE IN FIVE-YEAR AGE CATEGORIES |
| _AGE65YR | REPORTED AGE IN TWO AGE GROUPS CALCULATE |
| _AGE80 | IMPUTED AGE VALUE COLLAPSED ABOVE 80 |
| _AGE_G | IMPUTED AGE IN SIX GROUPS |
| WTKG3 | COMPUTED WEIGHT IN KILOGRAMS |
| _BMI5 | COMPUTED BODY MASS INDEX |
| _BMI5CAT | COMPUTED BODY MASS INDEX CATEGORIES |
| _RFBMI5 | OVERWEIGHT OR OBESE CALCULATED VARIABLE |
| _EDUCAG | COMPUTED LEVEL OF EDUCATION COMPLETED CA |
| _INCOMG1 | COMPUTED INCOME CATEGORIES |
| _SMOKER3 | COMPUTED SMOKING STATUS |
| _RFSMOK3 | CURRENT SMOKING CALCULATED VARIABLE |
| _CURECI1 | CURRENT E-CIGARETTE USER CALCULATED VARI |
| DRNKANY5 | DRINK ANY ALCOHOLIC BEVERAGES IN PAST 30 |
| _RFBING5 | BINGE DRINKING CALCULATED VARIABLE |
| _DRNKWK1 | COMPUTED NUMBER OF DRINKS OF ALCOHOL BEV |
| _RFDRHV7 | HEAVY ALCOHOL CONSUMPTION CALCULATED VA |
| _FLSHOT7 | FLU SHOT CALCULATED VARIABLE |
| _PNEUMO3 | PNEUMONIA VACCINATION CALCULATED VARIABL |
| _AIDTST4 | EVER BEEN TESTED FOR HIV CALCULATED VARI |
| FTJUDA2_ | COMPUTED FRUIT JUICE INTAKE IN TIMES PER |
| FRUTDA2_ | COMPUTED FRUIT INTAKE IN TIMES PER DAY |
| GRENDA1_ | COMPUTED DARK GREEN VEGETABLE INTAKE IN |
| FRNCHDA_ | FRENCH FRY INTAKE IN TIMES PER DAY |
| POTADA1_ | COMPUTED POTATO SERVINGS PER DAY |
| VEGEDA2_ | COMPUTED OTHER VEGETABLE INTAKE IN TIMES |
| _MISFRT1 | THE NUMBER OF MISSING FRUIT RESPONSES |
| _MISVEG1 | THE NUMBER OF MISSING VEGETABLE RESPONSE |
| _FRTRES1 | MISSING ANY FRUIT RESPONSES |
| _VEGRES1 | MISSING ANY VEGETABLE RESPONSES |
| _FRUTSU1 | TOTAL FRUITS CONSUMED PER DAY |
| _VEGESU1 | TOTAL VEGETABLES CONSUMED PER DAY |
| _FRTLT1A | CONSUME FRUIT 1 OR MORE TIMES PER DAY |
| _VEGLT1A | CONSUME VEGETABLES 1 OR MORE TIMES PER D |
| _FRT16A | REPORTED CONSUMING FRUIT >16/DAY |
| _VEG23A | REPORTED CONSUMING VEGETABLES >23/DAY |
| _FRUITE1 | FRUIT EXCLUSION FROM ANALYSES |
| _VEGETE1 | VEGETABLE EXCLUSION FROM ANALYSES |
| _MMSA | THE CODE OF METROPOLITAN OR MICROPOLITAN STATISTICAL AREA WHERE THE RESPONDENT LIVES |
| _MMSAWT | THE MMSA-LEVEL WEIGHT THAT IS USED WHEN GENERATING MMSA-LEVEL ESTIMATES FOR VARIABLES IN THE DATA SET |
| SEQNO | SEQUENCE NUMBER |
| MMSANAME | THE MMSA NAME |
| _resp_id | RESPONDENT ID |
7.3 Asthma and smoking BRFSS variables
The table below summarizes the variables of interest, combining information from https://www.cdc.gov/brfss/annual_data/2021/summary_matrix_21.html and https://www.cdc.gov/brfss/annual_data/2021/pdf/2021-calculated-variables-version4-508.pdf
| Output Variables (In Final Data Set) |
Description or Result of Calculation |
Values | |||||||||||||||
| _LTASTH1 | CV for lifetime asthma prevalence / Calculated variable for adults who have ever been told they have asthma. _LTASTH1 is derived from ASTHMA3. |
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| _CASTHM1 | CV for current asthma prevalence / Calculated variable for adults who have been told they currently have asthma. _CASTHM1 is derived from ASTHMA3 and ASTHNOW. |
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| _ASTHMS1 | Computed asthma status / Calculated variable for computed asthma status. _ASTHMS1 is derived from ASTHMA3 and ASTHNOW |
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| _SMOKER3 | Smoking Status: Everyday smoker, someday smoker, former smoker and non smoker / Calculated variable for four-level smoker status: everyday smoker, someday smoker, former smoker, non-smoker. _SMOKER3 is derived from SMOKE100 and SMOKDAY2. |
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| _RFSMOK3 | CV for current smoking status / _RFSMOK3 Calculated variable for adults who are current smokers. _RFSMOK3 is derived from _SMOKER3. |
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| _CURECI1 | CV for current e-cigarette smoker status / Calculated variable for adults who are current e-cigarette users. _CURECI1 is derived from ECIGNOW1. |
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